1. Field of Invention
The invention relates generally to a technique for correcting data obtained from a system, for which measurements obtained from the system are nonlinear functions of its interior properties, to remove blurring effects that are introduced during reconstruction processing of the data. In a particular implementation, the invention relates to deblurring of image data obtained using optical diffusion tomography.
2. Description of Related Art
Various techniques have been developed for processing data in image processing and other fields. For example, researchers in the field of diffuse optical tomography (DOT) had always assumed, explicitly or implicitly, that the major source of inaccuracies in the reconstructed images, especially for first order solutions, come from the non-linear dependence of measurement data on the medium's optical properties. DOT involves inserting energy such as light energy into a target medium such as human tissue and measuring the energy as it emerges from the medium. The energy is scattered in the medium due to varying optical properties in the medium, such as absorption and scattering. The problem is to determine the optical properties of the medium based on the detected energy. However, since the detector readings are nonlinear functions of the absorption and scattering coefficients [1], a non-linear technique is conventionally called for to solve the inverse problem. In particular, non-linear, iterative techniques such as Newton-Raphson are intended to take account of the inherent nonlinearity of the medium-measurement relationship. Such techniques involve solving a system of linear equations by making an initial estimate regarding the properties of the target medium, and solving the equations to obtain an updated estimate, then repeating the process with the new updated estimate. While this approach can yield good results, the computational expense is quite significant.
Accordingly, there is a need for a new approach for correcting data from a system that has a non-linear medium-measurement relationship to remove blurring effects that are introduced during processing of the data, which addresses the above and other issues.